class_docstr: asr keras solver
docstr: export saved_model
docstr_tok: ["export","saved_model"]
code: def export_model(self):
      """export saved_model"""
      mode = utils.INFER
      self.model_fn(mode=mode)
      assert self._built
  
      input_feat = self.model.get_layer("inputs").input
      input_length = self.model.get_layer("input_length").input
  
      def ctc_greedy_decode_lambda_func(args):
          y_pred, input_length = args
          input_length = tf.cast(input_length, dtype=tf.int32)
          decode_result, _ = ctc_greedy_decode(
              logits=y_pred,
              sequence_length=input_length,
              merge_repeated=True,
              blank_id=None,
          )
          return decode_result
  
      model_outputs = self.model.get_layer("outputs").output
      greedy_decode = Lambda(
          ctc_greedy_decode_lambda_func, output_shape=(), name="decode"
      )([model_outputs, input_length])
  
      model_to_export = Model(
          inputs=[input_feat, input_length], outputs=greedy_decode
      )
  
      model_export_path = Path(self._model_path).joinpath("export")
      model_to_export.save(
          filepath=str(model_export_path),
          overwrite=True,
          include_optimizer=True,
          save_format="tf",
          signatures=None,
          options=None,
      )
  
imports: from pathlib import Path
  from datetime import datetime
  from absl import logging
  import delta.compat
  from tensorflow.keras import backend
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import Lambda
  from delta import utils
  from delta.utils.decode import py_ctc
  from delta.utils import metrics
  from delta.utils.solver.keras_base_solver import KerasBaseSolver
  from delta.utils.register import registers
  from delta.utils.solver.utils.callbacks import TokenErrMetricCallBack
  from delta.utils.decode.tf_ctc import ctc_greedy_decode
label_desc: Use image node_id 13 for calling the AsrSolver obj's underlying member method code with example usage: obj.export_model() and returns: decode_result